Overview

Dataset statistics

Number of variables31
Number of observations142999
Missing cells52
Missing cells (%)< 0.1%
Duplicate rows3
Duplicate rows (%)< 0.1%
Total size in memory162.4 MiB
Average record size in memory1.2 KiB

Variable types

Categorical17
DateTime4
Numeric9
Unsupported1

Warnings

City has constant value "Palo Alto" Constant
State/Province has constant value "California" Constant
Country has constant value "United States" Constant
Dataset has 3 (< 0.1%) duplicate rows Duplicates
MAC Address has a high cardinality: 55 distinct values High cardinality
Park Duration (mins) has a high cardinality: 1217 distinct values High cardinality
Energy (kWh) is highly correlated with GHG Savings (kg) and 1 other fieldsHigh correlation
GHG Savings (kg) is highly correlated with Energy (kWh) and 1 other fieldsHigh correlation
Gasoline Savings (gallons) is highly correlated with Energy (kWh) and 1 other fieldsHigh correlation
Plug Type is highly correlated with Country and 3 other fieldsHigh correlation
Address 1 is highly correlated with Postal Code and 3 other fieldsHigh correlation
Port Number is highly correlated with Country and 2 other fieldsHigh correlation
Postal Code is highly correlated with Address 1 and 6 other fieldsHigh correlation
Org Name is highly correlated with Country and 2 other fieldsHigh correlation
Country is highly correlated with Plug Type and 14 other fieldsHigh correlation
Ended By is highly correlated with Country and 2 other fieldsHigh correlation
Start Time Zone is highly correlated with Country and 2 other fieldsHigh correlation
MAC Address is highly correlated with Postal Code and 4 other fieldsHigh correlation
City is highly correlated with Plug Type and 14 other fieldsHigh correlation
State/Province is highly correlated with Plug Type and 14 other fieldsHigh correlation
Currency is highly correlated with Country and 2 other fieldsHigh correlation
Station Name is highly correlated with Postal Code and 5 other fieldsHigh correlation
End Time Zone is highly correlated with Country and 2 other fieldsHigh correlation
Pairlocation is highly correlated with Postal Code and 4 other fieldsHigh correlation
Port Type is highly correlated with Plug Type and 3 other fieldsHigh correlation
Driver Postal Code is an unsupported type, check if it needs cleaning or further analysis Unsupported
Fee has 127421 (89.1%) zeros Zeros
ID has 10712 (7.5%) zeros Zeros

Reproduction

Analysis started2021-02-10 09:40:09.479774
Analysis finished2021-02-10 09:40:58.675659
Duration49.2 seconds
Software versionpandas-profiling v2.10.1
Download configurationconfig.yaml

Variables

Station Name
Categorical

HIGH CORRELATION

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.2 MiB
PALO ALTO CA / HAMILTON #2
14090 
PALO ALTO CA / BRYANT #2
11672 
PALO ALTO CA / BRYANT #1
10909 
PALO ALTO CA / HAMILTON #1
10712 
PALO ALTO CA / HIGH #4
10050 
Other values (22)
85566 

Length

Max length30
Median length25
Mean length24.81857216
Min length21

Characters and Unicode

Total characters3549031
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPALO ALTO CA / HAMILTON #1
2nd rowPALO ALTO CA / HAMILTON #1
3rd rowPALO ALTO CA / HAMILTON #1
4th rowPALO ALTO CA / HAMILTON #1
5th rowPALO ALTO CA / HAMILTON #1
ValueCountFrequency (%)
PALO ALTO CA / HAMILTON #214090
 
9.9%
PALO ALTO CA / BRYANT #211672
 
8.2%
PALO ALTO CA / BRYANT #110909
 
7.6%
PALO ALTO CA / HAMILTON #110712
 
7.5%
PALO ALTO CA / HIGH #410050
 
7.0%
PALO ALTO CA / WEBSTER #17760
 
5.4%
PALO ALTO CA / WEBSTER #27296
 
5.1%
PALO ALTO CA / HIGH #16756
 
4.7%
PALO ALTO CA / WEBSTER #35827
 
4.1%
PALO ALTO CA / CAMBRIDGE #15721
 
4.0%
Other values (17)52206
36.5%
2021-02-10T10:40:58.914453image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
142999
16.3%
alto142999
16.3%
palo142999
16.3%
ca142999
16.3%
152739
 
6.0%
246129
 
5.3%
bryant27693
 
3.2%
hamilton24802
 
2.8%
high21683
 
2.5%
webster20883
 
2.4%
Other values (10)110609
12.6%

Most occurring characters

ValueCountFrequency (%)
733535
20.7%
A518756
14.6%
L342108
9.6%
O334822
9.4%
T227341
 
6.4%
C167205
 
4.7%
P166731
 
4.7%
/142999
 
4.0%
#129941
 
3.7%
N84093
 
2.4%
Other values (17)701500
19.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2399557
67.6%
Space Separator733535
 
20.7%
Other Punctuation272940
 
7.7%
Decimal Number142999
 
4.0%

Most frequent character per category

ValueCountFrequency (%)
A518756
21.6%
L342108
14.3%
O334822
14.0%
T227341
9.5%
C167205
 
7.0%
P166731
 
6.9%
N84093
 
3.5%
I83749
 
3.5%
H73650
 
3.1%
B72782
 
3.0%
Other values (8)328320
13.7%
ValueCountFrequency (%)
152739
36.9%
246129
32.3%
316838
 
11.8%
415351
 
10.7%
66318
 
4.4%
55624
 
3.9%
ValueCountFrequency (%)
/142999
52.4%
#129941
47.6%
ValueCountFrequency (%)
733535
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2399557
67.6%
Common1149474
32.4%

Most frequent character per script

ValueCountFrequency (%)
A518756
21.6%
L342108
14.3%
O334822
14.0%
T227341
9.5%
C167205
 
7.0%
P166731
 
6.9%
N84093
 
3.5%
I83749
 
3.5%
H73650
 
3.1%
B72782
 
3.0%
Other values (8)328320
13.7%
ValueCountFrequency (%)
733535
63.8%
/142999
 
12.4%
#129941
 
11.3%
152739
 
4.6%
246129
 
4.0%
316838
 
1.5%
415351
 
1.3%
66318
 
0.5%
55624
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3549031
100.0%

Most frequent character per block

ValueCountFrequency (%)
733535
20.7%
A518756
14.6%
L342108
9.6%
O334822
9.4%
T227341
 
6.4%
C167205
 
4.7%
P166731
 
4.7%
/142999
 
4.0%
#129941
 
3.7%
N84093
 
2.4%
Other values (17)701500
19.8%

MAC Address
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct55
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
000D:6F00:015A:9D76
10712 
000D:6F00:009E:D39E
 
9759
000D6F0000A20D9E
 
7866
000D6F0000A20F47
 
6820
0024:B100:0002:1331
 
5871
Other values (50)
101971 

Length

Max length19
Median length19
Mean length18.25683396
Min length16

Characters and Unicode

Total characters2610709
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row000D:6F00:015A:9D76
2nd row000D:6F00:015A:9D76
3rd row000D:6F00:015A:9D76
4th row000D:6F00:015A:9D76
5th row000D:6F00:015A:9D76
ValueCountFrequency (%)
000D:6F00:015A:9D7610712
 
7.5%
000D:6F00:009E:D39E9759
 
6.8%
000D6F0000A20D9E7866
 
5.5%
000D6F0000A20F476820
 
4.8%
0024:B100:0002:13315871
 
4.1%
0024:B100:0002:13295721
 
4.0%
0024:B100:0002:10FE5544
 
3.9%
0024:B100:0002:13235482
 
3.8%
000D6F00009DCFC35477
 
3.8%
0024:B100:0002:13B75427
 
3.8%
Other values (45)74320
52.0%
2021-02-10T10:40:59.158224image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
000d:6f00:015a:9d7610712
 
7.5%
000d:6f00:009e:d39e9759
 
6.8%
000d6f0000a20d9e7866
 
5.5%
000d6f0000a20f476820
 
4.8%
0024:b100:0002:13315871
 
4.1%
0024:b100:0002:13295721
 
4.0%
0024:b100:0002:10fe5544
 
3.9%
0024:b100:0002:13235482
 
3.8%
000d6f00009dcfc35477
 
3.8%
0024:b100:0002:13b75427
 
3.8%
Other values (45)74320
52.0%

Most occurring characters

ValueCountFrequency (%)
01008170
38.6%
:322725
 
12.4%
2166042
 
6.4%
1159720
 
6.1%
D140007
 
5.4%
F126407
 
4.8%
6110250
 
4.2%
494467
 
3.6%
991721
 
3.5%
B86730
 
3.3%
Other values (7)304470
 
11.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1805216
69.1%
Uppercase Letter482768
 
18.5%
Other Punctuation322725
 
12.4%

Most frequent character per category

ValueCountFrequency (%)
01008170
55.8%
2166042
 
9.2%
1159720
 
8.8%
6110250
 
6.1%
494467
 
5.2%
991721
 
5.1%
365483
 
3.6%
551288
 
2.8%
746428
 
2.6%
811647
 
0.6%
ValueCountFrequency (%)
D140007
29.0%
F126407
26.2%
B86730
18.0%
A49670
 
10.3%
E48398
 
10.0%
C31556
 
6.5%
ValueCountFrequency (%)
:322725
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2127941
81.5%
Latin482768
 
18.5%

Most frequent character per script

ValueCountFrequency (%)
01008170
47.4%
:322725
 
15.2%
2166042
 
7.8%
1159720
 
7.5%
6110250
 
5.2%
494467
 
4.4%
991721
 
4.3%
365483
 
3.1%
551288
 
2.4%
746428
 
2.2%
ValueCountFrequency (%)
D140007
29.0%
F126407
26.2%
B86730
18.0%
A49670
 
10.3%
E48398
 
10.0%
C31556
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2610709
100.0%

Most frequent character per block

ValueCountFrequency (%)
01008170
38.6%
:322725
 
12.4%
2166042
 
6.4%
1159720
 
6.1%
D140007
 
5.4%
F126407
 
4.8%
6110250
 
4.2%
494467
 
3.6%
991721
 
3.5%
B86730
 
3.3%
Other values (7)304470
 
11.7%

Org Name
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.2 MiB
City of Palo Alto
122352 
City of Palo Alto
20647 

Length

Max length18
Median length18
Mean length17.85561437
Min length17

Characters and Unicode

Total characters2553335
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCity of Palo Alto
2nd rowCity of Palo Alto
3rd rowCity of Palo Alto
4th rowCity of Palo Alto
5th rowCity of Palo Alto
ValueCountFrequency (%)
City of Palo Alto 122352
85.6%
City of Palo Alto20647
 
14.4%
2021-02-10T10:40:59.369287image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-10T10:40:59.435359image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
palo142999
25.0%
of142999
25.0%
alto142999
25.0%
city142999
25.0%

Most occurring characters

ValueCountFrequency (%)
551349
21.6%
o428997
16.8%
t285998
11.2%
l285998
11.2%
C142999
 
5.6%
i142999
 
5.6%
y142999
 
5.6%
f142999
 
5.6%
P142999
 
5.6%
a142999
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1572989
61.6%
Space Separator551349
 
21.6%
Uppercase Letter428997
 
16.8%

Most frequent character per category

ValueCountFrequency (%)
o428997
27.3%
t285998
18.2%
l285998
18.2%
i142999
 
9.1%
y142999
 
9.1%
f142999
 
9.1%
a142999
 
9.1%
ValueCountFrequency (%)
C142999
33.3%
P142999
33.3%
A142999
33.3%
ValueCountFrequency (%)
551349
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2001986
78.4%
Common551349
 
21.6%

Most frequent character per script

ValueCountFrequency (%)
o428997
21.4%
t285998
14.3%
l285998
14.3%
C142999
 
7.1%
i142999
 
7.1%
y142999
 
7.1%
f142999
 
7.1%
P142999
 
7.1%
a142999
 
7.1%
A142999
 
7.1%
ValueCountFrequency (%)
551349
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2553335
100.0%

Most frequent character per block

ValueCountFrequency (%)
551349
21.6%
o428997
16.8%
t285998
11.2%
l285998
11.2%
C142999
 
5.6%
i142999
 
5.6%
y142999
 
5.6%
f142999
 
5.6%
P142999
 
5.6%
a142999
 
5.6%
Distinct136299
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum2011-07-29 20:17:00
Maximum2017-12-31 23:35:00
2021-02-10T10:40:59.537073image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:59.678633image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Start Time Zone
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
PDT
94497 
PST
48482 
UTC
 
20

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters428997
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPDT
2nd rowPDT
3rd rowPDT
4th rowPDT
5th rowPDT
ValueCountFrequency (%)
PDT94497
66.1%
PST48482
33.9%
UTC20
 
< 0.1%
2021-02-10T10:40:59.916087image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-10T10:40:59.980162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
pdt94497
66.1%
pst48482
33.9%
utc20
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T142999
33.3%
P142979
33.3%
D94497
22.0%
S48482
 
11.3%
U20
 
< 0.1%
C20
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter428997
100.0%

Most frequent character per category

ValueCountFrequency (%)
T142999
33.3%
P142979
33.3%
D94497
22.0%
S48482
 
11.3%
U20
 
< 0.1%
C20
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin428997
100.0%

Most frequent character per script

ValueCountFrequency (%)
T142999
33.3%
P142979
33.3%
D94497
22.0%
S48482
 
11.3%
U20
 
< 0.1%
C20
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII428997
100.0%

Most frequent character per block

ValueCountFrequency (%)
T142999
33.3%
P142979
33.3%
D94497
22.0%
S48482
 
11.3%
U20
 
< 0.1%
C20
 
< 0.1%
Distinct135841
Distinct (%)95.0%
Missing38
Missing (%)< 0.1%
Memory size1.1 MiB
Minimum2011-07-29 23:20:00
Maximum2018-01-01 07:18:00
2021-02-10T10:41:00.082577image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:41:00.230020image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

End Time Zone
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
PDT
94523 
PST
48475 
UTC
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters428997
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPDT
2nd rowPDT
3rd rowPDT
4th rowPDT
5th rowPDT
ValueCountFrequency (%)
PDT94523
66.1%
PST48475
33.9%
UTC1
 
< 0.1%
2021-02-10T10:41:00.453390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-10T10:41:00.515607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
pdt94523
66.1%
pst48475
33.9%
utc1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T142999
33.3%
P142998
33.3%
D94523
22.0%
S48475
 
11.3%
U1
 
< 0.1%
C1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter428997
100.0%

Most frequent character per category

ValueCountFrequency (%)
T142999
33.3%
P142998
33.3%
D94523
22.0%
S48475
 
11.3%
U1
 
< 0.1%
C1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin428997
100.0%

Most frequent character per script

ValueCountFrequency (%)
T142999
33.3%
P142998
33.3%
D94523
22.0%
S48475
 
11.3%
U1
 
< 0.1%
C1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII428997
100.0%

Most frequent character per block

ValueCountFrequency (%)
T142999
33.3%
P142998
33.3%
D94523
22.0%
S48475
 
11.3%
U1
 
< 0.1%
C1
 
< 0.1%
Distinct27265
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum1900-01-01 00:01:00
Maximum1900-01-01 23:57:22
2021-02-10T10:41:00.615946image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:41:00.755399image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct19102
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 22:55:55
2021-02-10T10:41:00.903345image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:41:01.041312image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Energy (kWh)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct105568
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.174862756
Minimum0
Maximum82.521561
Zeros27
Zeros (%)< 0.1%
Memory size1.1 MiB
2021-02-10T10:41:01.223574image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.833938
Q13.559181
median6.84
Q311.197
95-th percentile18.7446907
Maximum82.521561
Range82.521561
Interquartile range (IQR)7.637819

Descriptive statistics

Standard deviation6.679053777
Coefficient of variation (CV)0.8170233527
Kurtosis14.47341647
Mean8.174862756
Median Absolute Deviation (MAD)3.722037
Skewness2.659437342
Sum1168997.199
Variance44.60975936
MonotocityNot monotonic
2021-02-10T10:41:01.368852image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 × 10631
 
< 0.1%
027
 
< 0.1%
3 × 10622
 
< 0.1%
0.00820
 
< 0.1%
4 × 10619
 
< 0.1%
7 × 10617
 
< 0.1%
2 × 10617
 
< 0.1%
0.00116
 
< 0.1%
6.32116
 
< 0.1%
0.00216
 
< 0.1%
Other values (105558)142798
99.9%
ValueCountFrequency (%)
027
< 0.1%
1 × 10631
< 0.1%
2 × 10617
< 0.1%
3 × 10622
< 0.1%
4 × 10619
< 0.1%
5 × 10613
< 0.1%
6 × 10613
< 0.1%
7 × 10617
< 0.1%
8 × 10616
< 0.1%
9 × 10610
 
< 0.1%
ValueCountFrequency (%)
82.5215611
< 0.1%
81.991
< 0.1%
81.2421
< 0.1%
80.2217031
< 0.1%
79.5734861
< 0.1%
79.361
< 0.1%
79.3461
< 0.1%
79.2849731
< 0.1%
78.5685271
< 0.1%
78.5430761
< 0.1%

GHG Savings (kg)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12220
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.43344222
Minimum0
Maximum34.659
Zeros662
Zeros (%)0.5%
Memory size1.1 MiB
2021-02-10T10:41:01.512593image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.35
Q11.495
median2.873
Q34.703
95-th percentile7.873
Maximum34.659
Range34.659
Interquartile range (IQR)3.208

Descriptive statistics

Standard deviation2.80520194
Coefficient of variation (CV)0.8170231971
Kurtosis14.47338368
Mean3.43344222
Median Absolute Deviation (MAD)1.563
Skewness2.659433303
Sum490978.804
Variance7.869157922
MonotocityNot monotonic
2021-02-10T10:41:01.666393image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0662
 
0.5%
0.00395
 
0.1%
0.00190
 
0.1%
0.00478
 
0.1%
1.14158
 
< 0.1%
0.00258
 
< 0.1%
1.19654
 
< 0.1%
1.19754
 
< 0.1%
1.16253
 
< 0.1%
1.14352
 
< 0.1%
Other values (12210)141745
99.1%
ValueCountFrequency (%)
0662
0.5%
0.00190
 
0.1%
0.00258
 
< 0.1%
0.00395
 
0.1%
0.00478
 
0.1%
0.00549
 
< 0.1%
0.00646
 
< 0.1%
0.00729
 
< 0.1%
0.00832
 
< 0.1%
0.00940
 
< 0.1%
ValueCountFrequency (%)
34.6591
< 0.1%
34.4361
< 0.1%
34.1221
< 0.1%
33.6931
< 0.1%
33.4211
< 0.1%
33.3311
< 0.1%
33.3251
< 0.1%
33.31
< 0.1%
32.9991
< 0.1%
32.9881
< 0.1%

Gasoline Savings (gallons)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4999
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.025944503
Minimum0
Maximum10.356
Zeros766
Zeros (%)0.5%
Memory size1.1 MiB
2021-02-10T10:41:01.991445image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.105
Q10.447
median0.858
Q31.405
95-th percentile2.352
Maximum10.356
Range10.356
Interquartile range (IQR)0.958

Descriptive statistics

Standard deviation0.83822197
Coefficient of variation (CV)0.8170246708
Kurtosis14.47342848
Mean1.025944503
Median Absolute Deviation (MAD)0.467
Skewness2.659432661
Sum146709.038
Variance0.7026160709
MonotocityNot monotonic
2021-02-10T10:41:02.138351image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0766
 
0.5%
0.001245
 
0.2%
0.358168
 
0.1%
0.347157
 
0.1%
0.36157
 
0.1%
0.342151
 
0.1%
0.356150
 
0.1%
0.364149
 
0.1%
0.875149
 
0.1%
0.353148
 
0.1%
Other values (4989)140759
98.4%
ValueCountFrequency (%)
0766
0.5%
0.001245
 
0.2%
0.002126
 
0.1%
0.003109
 
0.1%
0.004104
 
0.1%
0.005104
 
0.1%
0.00687
 
0.1%
0.00788
 
0.1%
0.00872
 
0.1%
0.00977
 
0.1%
ValueCountFrequency (%)
10.3561
< 0.1%
10.291
< 0.1%
10.1961
< 0.1%
10.0681
< 0.1%
9.9861
< 0.1%
9.961
< 0.1%
9.9581
< 0.1%
9.951
< 0.1%
9.861
< 0.1%
9.8571
< 0.1%

Port Type
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
Level 2
138269 
Level 1
 
4730

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1000993
Distinct characters7
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLevel 2
2nd rowLevel 2
3rd rowLevel 2
4th rowLevel 2
5th rowLevel 2
ValueCountFrequency (%)
Level 2138269
96.7%
Level 14730
 
3.3%
2021-02-10T10:41:02.369084image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-10T10:41:02.432929image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
level142999
50.0%
2138269
48.3%
14730
 
1.7%

Most occurring characters

ValueCountFrequency (%)
e285998
28.6%
L142999
14.3%
v142999
14.3%
l142999
14.3%
142999
14.3%
2138269
13.8%
14730
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter571996
57.1%
Uppercase Letter142999
 
14.3%
Space Separator142999
 
14.3%
Decimal Number142999
 
14.3%

Most frequent character per category

ValueCountFrequency (%)
e285998
50.0%
v142999
25.0%
l142999
25.0%
ValueCountFrequency (%)
2138269
96.7%
14730
 
3.3%
ValueCountFrequency (%)
L142999
100.0%
ValueCountFrequency (%)
142999
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin714995
71.4%
Common285998
 
28.6%

Most frequent character per script

ValueCountFrequency (%)
e285998
40.0%
L142999
20.0%
v142999
20.0%
l142999
20.0%
ValueCountFrequency (%)
142999
50.0%
2138269
48.3%
14730
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000993
100.0%

Most frequent character per block

ValueCountFrequency (%)
e285998
28.6%
L142999
14.3%
v142999
14.3%
l142999
14.3%
142999
14.3%
2138269
13.8%
14730
 
0.5%

Port Number
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
2
90258 
1
52741 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters142999
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2
ValueCountFrequency (%)
290258
63.1%
152741
36.9%
2021-02-10T10:41:02.624143image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-10T10:41:02.686790image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
290258
63.1%
152741
36.9%

Most occurring characters

ValueCountFrequency (%)
290258
63.1%
152741
36.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number142999
100.0%

Most frequent character per category

ValueCountFrequency (%)
290258
63.1%
152741
36.9%

Most occurring scripts

ValueCountFrequency (%)
Common142999
100.0%

Most frequent character per script

ValueCountFrequency (%)
290258
63.1%
152741
36.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII142999
100.0%

Most frequent character per block

ValueCountFrequency (%)
290258
63.1%
152741
36.9%

Plug Type
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
J1772
138269 
NEMA 5-20R
 
4730

Length

Max length10
Median length5
Mean length5.165385772
Min length5

Characters and Unicode

Total characters738645
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJ1772
2nd rowJ1772
3rd rowJ1772
4th rowJ1772
5th rowJ1772
ValueCountFrequency (%)
J1772138269
96.7%
NEMA 5-20R4730
 
3.3%
2021-02-10T10:41:02.905580image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-10T10:41:02.971153image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
j1772138269
93.6%
5-20r4730
 
3.2%
nema4730
 
3.2%

Most occurring characters

ValueCountFrequency (%)
7276538
37.4%
2142999
19.4%
J138269
18.7%
1138269
18.7%
N4730
 
0.6%
E4730
 
0.6%
M4730
 
0.6%
A4730
 
0.6%
4730
 
0.6%
54730
 
0.6%
Other values (3)14190
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number567266
76.8%
Uppercase Letter161919
 
21.9%
Space Separator4730
 
0.6%
Dash Punctuation4730
 
0.6%

Most frequent character per category

ValueCountFrequency (%)
J138269
85.4%
N4730
 
2.9%
E4730
 
2.9%
M4730
 
2.9%
A4730
 
2.9%
R4730
 
2.9%
ValueCountFrequency (%)
7276538
48.7%
2142999
25.2%
1138269
24.4%
54730
 
0.8%
04730
 
0.8%
ValueCountFrequency (%)
4730
100.0%
ValueCountFrequency (%)
-4730
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common576726
78.1%
Latin161919
 
21.9%

Most frequent character per script

ValueCountFrequency (%)
7276538
47.9%
2142999
24.8%
1138269
24.0%
4730
 
0.8%
54730
 
0.8%
-4730
 
0.8%
04730
 
0.8%
ValueCountFrequency (%)
J138269
85.4%
N4730
 
2.9%
E4730
 
2.9%
M4730
 
2.9%
A4730
 
2.9%
R4730
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII738645
100.0%

Most frequent character per block

ValueCountFrequency (%)
7276538
37.4%
2142999
19.4%
J138269
18.7%
1138269
18.7%
N4730
 
0.6%
E4730
 
0.6%
M4730
 
0.6%
A4730
 
0.6%
4730
 
0.6%
54730
 
0.6%
Other values (3)14190
 
1.9%

Address 1
Categorical

HIGH CORRELATION

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
250 Hamilton Ave
24802 
Bryant St
21356 
528 High St
19677 
3700 Middlefield Rd
18250 
532 Webster St
11683 
Other values (12)
47231 

Length

Max length19
Median length14
Mean length14.10330142
Min length9

Characters and Unicode

Total characters2016758
Distinct characters41
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row250 Hamilton Ave
2nd row250 Hamilton Ave
3rd row250 Hamilton Ave
4th row250 Hamilton Ave
5th row250 Hamilton Ave
ValueCountFrequency (%)
250 Hamilton Ave24802
17.3%
Bryant St21356
14.9%
528 High St19677
13.8%
3700 Middlefield Rd18250
12.8%
532 Webster St11683
8.2%
400 Cambridge Ave11148
7.8%
520 Webster St8823
 
6.2%
1213 Newell Rd7106
 
5.0%
445 Bryant St6337
 
4.4%
275 Cambridge Ave5482
 
3.8%
Other values (7)8335
 
5.8%
2021-02-10T10:41:03.174749image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
st70259
17.2%
ave41432
 
10.2%
bryant27693
 
6.8%
rd25356
 
6.2%
25024802
 
6.1%
hamilton24802
 
6.1%
52821683
 
5.3%
high21683
 
5.3%
webster20854
 
5.1%
370018250
 
4.5%
Other values (16)110827
27.2%

Most occurring characters

ValueCountFrequency (%)
264642
 
13.1%
e150511
 
7.5%
t149560
 
7.4%
i99615
 
4.9%
096939
 
4.8%
d96736
 
4.8%
585487
 
4.2%
283749
 
4.2%
l81466
 
4.0%
a75077
 
3.7%
Other values (31)832976
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1070008
53.1%
Decimal Number397281
 
19.7%
Uppercase Letter283648
 
14.1%
Space Separator264642
 
13.1%
Other Punctuation831
 
< 0.1%
Dash Punctuation348
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e150511
14.1%
t149560
14.0%
i99615
9.3%
d96736
9.0%
l81466
 
7.6%
a75077
 
7.0%
r71158
 
6.7%
n58447
 
5.5%
m41432
 
3.9%
v41432
 
3.9%
Other values (10)204574
19.1%
ValueCountFrequency (%)
S69084
24.4%
H45310
16.0%
A41432
14.6%
B27693
9.8%
W26806
 
9.5%
R25356
 
8.9%
M18250
 
6.4%
C16659
 
5.9%
N7106
 
2.5%
D5952
 
2.1%
ValueCountFrequency (%)
096939
24.4%
585487
21.5%
283749
21.1%
337409
 
9.4%
425970
 
6.5%
723732
 
6.0%
122312
 
5.6%
821683
 
5.5%
ValueCountFrequency (%)
264642
100.0%
ValueCountFrequency (%)
-348
100.0%
ValueCountFrequency (%)
.831
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1353656
67.1%
Common663102
32.9%

Most frequent character per script

ValueCountFrequency (%)
e150511
 
11.1%
t149560
 
11.0%
i99615
 
7.4%
d96736
 
7.1%
l81466
 
6.0%
a75077
 
5.5%
r71158
 
5.3%
S69084
 
5.1%
n58447
 
4.3%
H45310
 
3.3%
Other values (20)456692
33.7%
ValueCountFrequency (%)
264642
39.9%
096939
 
14.6%
585487
 
12.9%
283749
 
12.6%
337409
 
5.6%
425970
 
3.9%
723732
 
3.6%
122312
 
3.4%
821683
 
3.3%
.831
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2016758
100.0%

Most frequent character per block

ValueCountFrequency (%)
264642
 
13.1%
e150511
 
7.5%
t149560
 
7.4%
i99615
 
4.9%
096939
 
4.8%
d96736
 
4.8%
585487
 
4.2%
283749
 
4.2%
l81466
 
4.0%
a75077
 
3.7%
Other values (31)832976
41.3%

City
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.0 MiB
Palo Alto
142999 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1286991
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPalo Alto
2nd rowPalo Alto
3rd rowPalo Alto
4th rowPalo Alto
5th rowPalo Alto
ValueCountFrequency (%)
Palo Alto142999
100.0%
2021-02-10T10:41:03.374918image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-10T10:41:03.438339image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
palo142999
50.0%
alto142999
50.0%

Most occurring characters

ValueCountFrequency (%)
l285998
22.2%
o285998
22.2%
P142999
11.1%
a142999
11.1%
142999
11.1%
A142999
11.1%
t142999
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter857994
66.7%
Uppercase Letter285998
 
22.2%
Space Separator142999
 
11.1%

Most frequent character per category

ValueCountFrequency (%)
l285998
33.3%
o285998
33.3%
a142999
16.7%
t142999
16.7%
ValueCountFrequency (%)
P142999
50.0%
A142999
50.0%
ValueCountFrequency (%)
142999
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1143992
88.9%
Common142999
 
11.1%

Most frequent character per script

ValueCountFrequency (%)
l285998
25.0%
o285998
25.0%
P142999
12.5%
a142999
12.5%
A142999
12.5%
t142999
12.5%
ValueCountFrequency (%)
142999
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1286991
100.0%

Most frequent character per block

ValueCountFrequency (%)
l285998
22.2%
o285998
22.2%
P142999
11.1%
a142999
11.1%
142999
11.1%
A142999
11.1%
t142999
11.1%

State/Province
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 MiB
California
142999 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1429990
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCalifornia
2nd rowCalifornia
3rd rowCalifornia
4th rowCalifornia
5th rowCalifornia
ValueCountFrequency (%)
California142999
100.0%
2021-02-10T10:41:03.633462image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-10T10:41:03.704632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
california142999
100.0%

Most occurring characters

ValueCountFrequency (%)
a285998
20.0%
i285998
20.0%
C142999
10.0%
l142999
10.0%
f142999
10.0%
o142999
10.0%
r142999
10.0%
n142999
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1286991
90.0%
Uppercase Letter142999
 
10.0%

Most frequent character per category

ValueCountFrequency (%)
a285998
22.2%
i285998
22.2%
l142999
11.1%
f142999
11.1%
o142999
11.1%
r142999
11.1%
n142999
11.1%
ValueCountFrequency (%)
C142999
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1429990
100.0%

Most frequent character per script

ValueCountFrequency (%)
a285998
20.0%
i285998
20.0%
C142999
10.0%
l142999
10.0%
f142999
10.0%
o142999
10.0%
r142999
10.0%
n142999
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1429990
100.0%

Most frequent character per block

ValueCountFrequency (%)
a285998
20.0%
i285998
20.0%
C142999
10.0%
l142999
10.0%
f142999
10.0%
o142999
10.0%
r142999
10.0%
n142999
10.0%

Postal Code
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
94301
95061 
94303
31308 
94306
16630 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters714995
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row94301
2nd row94301
3rd row94301
4th row94301
5th row94301
ValueCountFrequency (%)
9430195061
66.5%
9430331308
 
21.9%
9430616630
 
11.6%
2021-02-10T10:41:03.923783image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-10T10:41:03.988037image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
9430195061
66.5%
9430331308
 
21.9%
9430616630
 
11.6%

Most occurring characters

ValueCountFrequency (%)
3174307
24.4%
9142999
20.0%
4142999
20.0%
0142999
20.0%
195061
13.3%
616630
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number714995
100.0%

Most frequent character per category

ValueCountFrequency (%)
3174307
24.4%
9142999
20.0%
4142999
20.0%
0142999
20.0%
195061
13.3%
616630
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common714995
100.0%

Most frequent character per script

ValueCountFrequency (%)
3174307
24.4%
9142999
20.0%
4142999
20.0%
0142999
20.0%
195061
13.3%
616630
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII714995
100.0%

Most frequent character per block

ValueCountFrequency (%)
3174307
24.4%
9142999
20.0%
4142999
20.0%
0142999
20.0%
195061
13.3%
616630
 
2.3%

Country
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.5 MiB
United States
142999 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters1858987
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowUnited States
4th rowUnited States
5th rowUnited States
ValueCountFrequency (%)
United States142999
100.0%
2021-02-10T10:41:04.177542image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-10T10:41:04.239489image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
states142999
50.0%
united142999
50.0%

Most occurring characters

ValueCountFrequency (%)
t428997
23.1%
e285998
15.4%
U142999
 
7.7%
n142999
 
7.7%
i142999
 
7.7%
d142999
 
7.7%
142999
 
7.7%
S142999
 
7.7%
a142999
 
7.7%
s142999
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1429990
76.9%
Uppercase Letter285998
 
15.4%
Space Separator142999
 
7.7%

Most frequent character per category

ValueCountFrequency (%)
t428997
30.0%
e285998
20.0%
n142999
 
10.0%
i142999
 
10.0%
d142999
 
10.0%
a142999
 
10.0%
s142999
 
10.0%
ValueCountFrequency (%)
U142999
50.0%
S142999
50.0%
ValueCountFrequency (%)
142999
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1715988
92.3%
Common142999
 
7.7%

Most frequent character per script

ValueCountFrequency (%)
t428997
25.0%
e285998
16.7%
U142999
 
8.3%
n142999
 
8.3%
i142999
 
8.3%
d142999
 
8.3%
S142999
 
8.3%
a142999
 
8.3%
s142999
 
8.3%
ValueCountFrequency (%)
142999
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1858987
100.0%

Most frequent character per block

ValueCountFrequency (%)
t428997
23.1%
e285998
15.4%
U142999
 
7.7%
n142999
 
7.7%
i142999
 
7.7%
d142999
 
7.7%
142999
 
7.7%
S142999
 
7.7%
a142999
 
7.7%
s142999
 
7.7%

Latitude
Real number (ℝ≥0)

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.4405362
Minimum37.422104
Maximum37.449375
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-02-10T10:41:04.322046image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum37.422104
5-th percentile37.422123
Q137.443127
median37.444489
Q337.446583
95-th percentile37.448284
Maximum37.449375
Range0.027271
Interquartile range (IQR)0.003456

Descriptive statistics

Standard deviation0.009351716199
Coefficient of variation (CV)0.0002497751675
Kurtosis-0.4264972262
Mean37.4405362
Median Absolute Deviation (MAD)0.002094
Skewness-1.157793992
Sum5353959.236
Variance8.745459587 × 105
MonotocityNot monotonic
2021-02-10T10:41:04.452966image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
37.4465111595
 
8.1%
37.44661310822
 
7.6%
37.44448910070
 
7.0%
37.4436769027
 
6.3%
37.4482847935
 
5.5%
37.4454967815
 
5.5%
37.4446077143
 
5.0%
37.4482387132
 
5.0%
37.4435586729
 
4.7%
37.4443595872
 
4.1%
Other values (30)58859
41.2%
ValueCountFrequency (%)
37.4221044665
3.3%
37.4221191242
 
0.9%
37.4221234722
3.3%
37.4221385360
3.7%
37.422146913
 
0.6%
37.4221691348
 
0.9%
37.4261554990
3.5%
37.4261824691
3.3%
37.42733736
 
0.5%
37.427349731
 
0.5%
ValueCountFrequency (%)
37.449375164
 
0.1%
37.4493484740
3.3%
37.4482847935
5.5%
37.448273912
 
0.6%
37.4482387132
5.0%
37.446785636
 
0.4%
37.44669902
 
0.6%
37.446667958
 
0.7%
37.44661310822
7.6%
37.4465832616
 
1.8%

Longitude
Real number (ℝ)

Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.1510521
Minimum-122.163239
Maximum-122.11248
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-02-10T10:41:04.573469image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-122.163239
5-th percentile-122.16288
Q1-122.16214
median-122.159904
Q3-122.144188
95-th percentile-122.113457
Maximum-122.11248
Range0.050759
Interquartile range (IQR)0.017952

Descriptive statistics

Standard deviation0.01632732565
Coefficient of variation (CV)-0.0001336650431
Kurtosis0.7881561663
Mean-122.1510521
Median Absolute Deviation (MAD)0.002404
Skewness1.458586382
Sum-17467478.3
Variance0.0002665815628
MonotocityNot monotonic
2021-02-10T10:41:04.707768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
-122.1621411595
 
8.1%
-122.16230810822
 
7.6%
-122.1603710070
 
7.0%
-122.1460659681
 
6.8%
-122.162889027
 
6.3%
-122.1582727576
 
5.3%
-122.1602637143
 
5.0%
-122.1582037132
 
5.0%
-122.1628496729
 
4.7%
-122.1599045872
 
4.1%
Other values (33)57352
40.1%
ValueCountFrequency (%)
-122.16323923
 
< 0.1%
-122.16323123
 
< 0.1%
-122.16313940
 
< 0.1%
-122.16307827
 
< 0.1%
-122.1630481000
 
0.7%
-122.1630332497
 
1.7%
-122.1630172317
 
1.6%
-122.162889027
6.3%
-122.1628496729
4.7%
-122.162369636
 
0.4%
ValueCountFrequency (%)
-122.112481348
 
0.9%
-122.112511913
 
0.6%
-122.1125561242
 
0.9%
-122.1134575360
3.7%
-122.1134874722
3.3%
-122.113514665
3.3%
-122.138847637
 
0.4%
-122.1388633849
2.7%
-122.1388933467
2.4%
-122.1389243966
2.8%

Currency
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
USD
142990 
MXN
 
5
EUR
 
3
CAD
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters428997
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD
ValueCountFrequency (%)
USD142990
> 99.9%
MXN5
 
< 0.1%
EUR3
 
< 0.1%
CAD1
 
< 0.1%
2021-02-10T10:41:04.956514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-10T10:41:05.022559image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
usd142990
> 99.9%
mxn5
 
< 0.1%
eur3
 
< 0.1%
cad1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
U142993
33.3%
D142991
33.3%
S142990
33.3%
M5
 
< 0.1%
X5
 
< 0.1%
N5
 
< 0.1%
E3
 
< 0.1%
R3
 
< 0.1%
C1
 
< 0.1%
A1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter428997
100.0%

Most frequent character per category

ValueCountFrequency (%)
U142993
33.3%
D142991
33.3%
S142990
33.3%
M5
 
< 0.1%
X5
 
< 0.1%
N5
 
< 0.1%
E3
 
< 0.1%
R3
 
< 0.1%
C1
 
< 0.1%
A1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin428997
100.0%

Most frequent character per script

ValueCountFrequency (%)
U142993
33.3%
D142991
33.3%
S142990
33.3%
M5
 
< 0.1%
X5
 
< 0.1%
N5
 
< 0.1%
E3
 
< 0.1%
R3
 
< 0.1%
C1
 
< 0.1%
A1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII428997
100.0%

Most frequent character per block

ValueCountFrequency (%)
U142993
33.3%
D142991
33.3%
S142990
33.3%
M5
 
< 0.1%
X5
 
< 0.1%
N5
 
< 0.1%
E3
 
< 0.1%
R3
 
< 0.1%
C1
 
< 0.1%
A1
 
< 0.1%

Fee
Real number (ℝ≥0)

ZEROS

Distinct886
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2200582522
Minimum0
Maximum79.94
Zeros127421
Zeros (%)89.1%
Memory size1.1 MiB
2021-02-10T10:41:05.134666image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.74
Maximum79.94
Range79.94
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9127005777
Coefficient of variation (CV)4.147540793
Kurtosis604.2019228
Mean0.2200582522
Median Absolute Deviation (MAD)0
Skewness13.59793344
Sum31468.11
Variance0.8330223445
MonotocityNot monotonic
2021-02-10T10:41:05.272849image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0127421
89.1%
1.4689
 
0.1%
1.4384
 
0.1%
1.4783
 
0.1%
1.482
 
0.1%
1.4280
 
0.1%
1.4179
 
0.1%
1.4579
 
0.1%
1.4475
 
0.1%
1.5371
 
< 0.1%
Other values (876)14856
 
10.4%
ValueCountFrequency (%)
0127421
89.1%
0.0112
 
< 0.1%
0.027
 
< 0.1%
0.0314
 
< 0.1%
0.0418
 
< 0.1%
0.0516
 
< 0.1%
0.0628
 
< 0.1%
0.0727
 
< 0.1%
0.0834
 
< 0.1%
0.0927
 
< 0.1%
ValueCountFrequency (%)
79.941
< 0.1%
47.561
< 0.1%
44.21
< 0.1%
31.691
< 0.1%
31.431
< 0.1%
28.151
< 0.1%
27.141
< 0.1%
24.221
< 0.1%
23.711
< 0.1%
23.381
< 0.1%

Ended By
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
Plug Out at Vehicle
107878 
Customer
32844 
CPS Server
 
2006
Final GFCI Tripped
 
64
Door Open
 
56
Other values (8)
 
151

Length

Max length28
Median length19
Mean length16.33896741
Min length3

Characters and Unicode

Total characters2336456
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPlug Out at Vehicle
2nd rowCustomer
3rd rowPlug Out at Vehicle
4th rowCustomer
5th rowPlug Out at Vehicle
ValueCountFrequency (%)
Plug Out at Vehicle107878
75.4%
Customer32844
 
23.0%
CPS Server2006
 
1.4%
Final GFCI Tripped64
 
< 0.1%
Door Open56
 
< 0.1%
Outlet Unreachable50
 
< 0.1%
Door34
 
< 0.1%
Plug Removed While Rebooting24
 
< 0.1%
Unknown17
 
< 0.1%
Plug Out at Station12
 
< 0.1%
Other values (3)14
 
< 0.1%
2021-02-10T10:41:05.524966image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
plug107914
23.0%
out107890
23.0%
at107890
23.0%
vehicle107878
23.0%
customer32844
 
7.0%
server2006
 
0.4%
cps2006
 
0.4%
door90
 
< 0.1%
final71
 
< 0.1%
gfci71
 
< 0.1%
Other values (14)347
 
0.1%

Most occurring characters

ValueCountFrequency (%)
326008
14.0%
e252990
10.8%
t248791
10.6%
u248703
10.6%
l215997
9.2%
P109925
 
4.7%
i108092
 
4.6%
a108073
 
4.6%
O107998
 
4.6%
h107952
 
4.6%
Other values (27)501927
21.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1645102
70.4%
Uppercase Letter365332
 
15.6%
Space Separator326008
 
14.0%
Decimal Number7
 
< 0.1%
Dash Punctuation7
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e252990
15.4%
t248791
15.1%
u248703
15.1%
l215997
13.1%
i108092
6.6%
a108073
6.6%
h107952
6.6%
g107943
6.6%
c107928
6.6%
r37079
 
2.3%
Other values (10)101554
6.2%
ValueCountFrequency (%)
P109925
30.1%
O107998
29.6%
V107878
29.5%
C34923
 
9.6%
S4024
 
1.1%
F142
 
< 0.1%
D92
 
< 0.1%
G71
 
< 0.1%
I71
 
< 0.1%
U67
 
< 0.1%
Other values (4)141
 
< 0.1%
ValueCountFrequency (%)
326008
100.0%
ValueCountFrequency (%)
47
100.0%
ValueCountFrequency (%)
-7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2010434
86.0%
Common326022
 
14.0%

Most frequent character per script

ValueCountFrequency (%)
e252990
12.6%
t248791
12.4%
u248703
12.4%
l215997
10.7%
P109925
 
5.5%
i108092
 
5.4%
a108073
 
5.4%
O107998
 
5.4%
h107952
 
5.4%
g107943
 
5.4%
Other values (24)393970
19.6%
ValueCountFrequency (%)
326008
> 99.9%
47
 
< 0.1%
-7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2336456
100.0%

Most frequent character per block

ValueCountFrequency (%)
326008
14.0%
e252990
10.8%
t248791
10.6%
u248703
10.6%
l215997
9.2%
P109925
 
4.7%
i108092
 
4.6%
a108073
 
4.6%
O107998
 
4.6%
h107952
 
4.6%
Other values (27)501927
21.5%

Plug In Event Id
Real number (ℝ≥0)

Distinct24117
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118419607
Minimum1
Maximum1514779105
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-02-10T10:41:05.657254image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile239
Q11294
median3136
Q35623
95-th percentile1466483068
Maximum1514779105
Range1514779104
Interquartile range (IQR)4329

Descriptive statistics

Standard deviation400359258.3
Coefficient of variation (CV)3.380852787
Kurtosis7.536082849
Mean118419607
Median Absolute Deviation (MAD)2053
Skewness3.086996533
Sum1.693388538 × 1013
Variance1.602875357 × 1017
MonotocityNot monotonic
2021-02-10T10:41:05.810469image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
657217
 
0.2%
43237
 
< 0.1%
34536
 
< 0.1%
31936
 
< 0.1%
14036
 
< 0.1%
28636
 
< 0.1%
46036
 
< 0.1%
53235
 
< 0.1%
37135
 
< 0.1%
36235
 
< 0.1%
Other values (24107)142460
99.6%
ValueCountFrequency (%)
15
 
< 0.1%
29
< 0.1%
39
< 0.1%
414
< 0.1%
514
< 0.1%
617
< 0.1%
719
< 0.1%
816
< 0.1%
920
< 0.1%
1018
< 0.1%
ValueCountFrequency (%)
15147791051
< 0.1%
15147641571
< 0.1%
15147511111
< 0.1%
15147495711
< 0.1%
15147443371
< 0.1%
15147366121
< 0.1%
15146820081
< 0.1%
15146817021
< 0.1%
15146810581
< 0.1%
15146728811
< 0.1%

Driver Postal Code
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size6.2 MiB

Park Duration (mins)
Categorical

HIGH CARDINALITY

Distinct1217
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
2
 
767
182
 
719
181
 
702
176
 
683
183
 
681
Other values (1212)
139447 

Length

Max length8
Median length5
Mean length4.613018273
Min length3

Characters and Unicode

Total characters659657
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique192 ?
Unique (%)0.1%

Sample

1st row 184
2nd row 2
3rd row 258
4th row 123
5th row 71
ValueCountFrequency (%)
2 767
 
0.5%
182 719
 
0.5%
181 702
 
0.5%
176 683
 
0.5%
183 681
 
0.5%
179 678
 
0.5%
177 676
 
0.5%
101 669
 
0.5%
66 665
 
0.5%
73 658
 
0.5%
Other values (1207)136101
95.2%
2021-02-10T10:41:06.101555image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2767
 
0.5%
182719
 
0.5%
181702
 
0.5%
176683
 
0.5%
183681
 
0.5%
179678
 
0.5%
177676
 
0.5%
101669
 
0.5%
66665
 
0.5%
73658
 
0.5%
Other values (1207)136101
95.2%

Most occurring characters

ValueCountFrequency (%)
285998
43.4%
187570
 
13.3%
249658
 
7.5%
332953
 
5.0%
530322
 
4.6%
730145
 
4.6%
630069
 
4.6%
430053
 
4.6%
829306
 
4.4%
927238
 
4.1%
Other values (2)26345
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number373353
56.6%
Space Separator285998
43.4%
Other Punctuation306
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
187570
23.5%
249658
13.3%
332953
 
8.8%
530322
 
8.1%
730145
 
8.1%
630069
 
8.1%
430053
 
8.0%
829306
 
7.8%
927238
 
7.3%
026039
 
7.0%
ValueCountFrequency (%)
285998
100.0%
ValueCountFrequency (%)
,306
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common659657
100.0%

Most frequent character per script

ValueCountFrequency (%)
285998
43.4%
187570
 
13.3%
249658
 
7.5%
332953
 
5.0%
530322
 
4.6%
730145
 
4.6%
630069
 
4.6%
430053
 
4.6%
829306
 
4.4%
927238
 
4.1%
Other values (2)26345
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII659657
100.0%

Most frequent character per block

ValueCountFrequency (%)
285998
43.4%
187570
 
13.3%
249658
 
7.5%
332953
 
5.0%
530322
 
4.6%
730145
 
4.6%
630069
 
4.6%
430053
 
4.6%
829306
 
4.4%
927238
 
4.1%
Other values (2)26345
 
4.0%

Charge Duration (mins)
Real number (ℝ≥0)

Distinct775
Distinct (%)0.5%
Missing14
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean121.5628283
Minimum0
Maximum972
Zeros697
Zeros (%)0.5%
Memory size1.1 MiB
2021-02-10T10:41:06.214740image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q164
median112
Q3167
95-th percentile251
Maximum972
Range972
Interquartile range (IQR)103

Descriptive statistics

Standard deviation81.64789703
Coefficient of variation (CV)0.671651838
Kurtosis7.849950593
Mean121.5628283
Median Absolute Deviation (MAD)51
Skewness1.71271298
Sum17381661
Variance6666.37909
MonotocityNot monotonic
2021-02-10T10:41:06.579116image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86929
 
0.6%
88924
 
0.6%
1922
 
0.6%
85914
 
0.6%
87913
 
0.6%
89874
 
0.6%
84850
 
0.6%
83831
 
0.6%
132821
 
0.6%
130820
 
0.6%
Other values (765)134187
93.8%
ValueCountFrequency (%)
0697
0.5%
1922
0.6%
2590
0.4%
3421
0.3%
4332
 
0.2%
5388
0.3%
6358
 
0.3%
7412
0.3%
8377
0.3%
9367
 
0.3%
ValueCountFrequency (%)
9721
< 0.1%
9671
< 0.1%
9401
< 0.1%
9321
< 0.1%
9252
< 0.1%
9231
< 0.1%
9111
< 0.1%
9061
< 0.1%
9021
< 0.1%
8911
< 0.1%

ID
Real number (ℝ≥0)

ZEROS

Distinct55
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.55533255
Minimum0
Maximum54
Zeros10712
Zeros (%)7.5%
Memory size1.1 MiB
2021-02-10T10:41:06.732785image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median23
Q335
95-th percentile44
Maximum54
Range54
Interquartile range (IQR)31

Descriptive statistics

Standard deviation15.45231679
Coefficient of variation (CV)0.7168674737
Kurtosis-1.366341342
Mean21.55533255
Median Absolute Deviation (MAD)14
Skewness-0.05956427579
Sum3082391
Variance238.7740943
MonotocityNot monotonic
2021-02-10T10:41:06.914250image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010712
 
7.5%
19759
 
6.8%
47866
 
5.5%
26820
 
4.8%
345871
 
4.1%
355721
 
4.0%
325544
 
3.9%
385482
 
3.8%
55477
 
3.8%
375427
 
3.8%
Other values (45)74320
52.0%
ValueCountFrequency (%)
010712
7.5%
19759
6.8%
26820
4.8%
32741
 
1.9%
47866
5.5%
55477
3.8%
6857
 
0.6%
71599
 
1.1%
8495
 
0.3%
9252
 
0.2%
ValueCountFrequency (%)
5424
 
< 0.1%
534
 
< 0.1%
52456
0.3%
51620
0.4%
50842
0.6%
491088
0.8%
48899
0.6%
47902
0.6%
46636
0.4%
45958
0.7%

Pairlocation
Categorical

HIGH CORRELATION

Distinct44
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.6 MiB
37.44651x-122.16214
11595 
37.446613x-122.162308
10822 
37.444489x-122.16037
10070 
37.443676x-122.16288
 
9027
37.448284x-122.158272
 
7576
Other values (39)
93909 

Length

Max length21
Median length21
Mean length20.63044497
Min length18

Characters and Unicode

Total characters2950133
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row37.444572x-122.160309
2nd row37.444572x-122.160309
3rd row37.444572x-122.160309
4th row37.444572x-122.160309
5th row37.444572x-122.160309
ValueCountFrequency (%)
37.44651x-122.1621411595
 
8.1%
37.446613x-122.16230810822
 
7.6%
37.444489x-122.1603710070
 
7.0%
37.443676x-122.162889027
 
6.3%
37.448284x-122.1582727576
 
5.3%
37.444607x-122.1602637143
 
5.0%
37.448238x-122.1582037132
 
5.0%
37.443558x-122.1628496729
 
4.7%
37.444359x-122.1599045872
 
4.1%
37.428318x-122.1441885482
 
3.8%
Other values (34)61551
43.0%
2021-02-10T10:41:07.197393image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
37.44651x-122.1621411595
 
8.1%
37.446613x-122.16230810822
 
7.6%
37.444489x-122.1603710070
 
7.0%
37.443676x-122.162889027
 
6.3%
37.448284x-122.1582727576
 
5.3%
37.444607x-122.1602637143
 
5.0%
37.448238x-122.1582037132
 
5.0%
37.443558x-122.1628496729
 
4.7%
37.444359x-122.1599045872
 
4.1%
37.428318x-122.1441885482
 
3.8%
Other values (34)61551
43.0%

Most occurring characters

ValueCountFrequency (%)
2452464
15.3%
1397647
13.5%
4385803
13.1%
3302400
10.3%
.285998
9.7%
7210890
7.1%
6197155
6.7%
8172980
 
5.9%
x142999
 
4.8%
-142999
 
4.8%
Other values (3)258798
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2378137
80.6%
Other Punctuation285998
 
9.7%
Lowercase Letter142999
 
4.8%
Dash Punctuation142999
 
4.8%

Most frequent character per category

ValueCountFrequency (%)
2452464
19.0%
1397647
16.7%
4385803
16.2%
3302400
12.7%
7210890
8.9%
6197155
8.3%
8172980
 
7.3%
5109346
 
4.6%
975033
 
3.2%
074419
 
3.1%
ValueCountFrequency (%)
.285998
100.0%
ValueCountFrequency (%)
x142999
100.0%
ValueCountFrequency (%)
-142999
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2807134
95.2%
Latin142999
 
4.8%

Most frequent character per script

ValueCountFrequency (%)
2452464
16.1%
1397647
14.2%
4385803
13.7%
3302400
10.8%
.285998
10.2%
7210890
7.5%
6197155
7.0%
8172980
 
6.2%
-142999
 
5.1%
5109346
 
3.9%
Other values (2)149452
 
5.3%
ValueCountFrequency (%)
x142999
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2950133
100.0%

Most frequent character per block

ValueCountFrequency (%)
2452464
15.3%
1397647
13.5%
4385803
13.1%
3302400
10.3%
.285998
9.7%
7210890
7.1%
6197155
6.7%
8172980
 
5.9%
x142999
 
4.8%
-142999
 
4.8%
Other values (3)258798
8.8%

Interactions

2021-02-10T10:40:44.803985image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:44.986974image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:45.125661image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:45.253503image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:45.388952image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:45.541622image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:45.683601image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:45.826334image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:45.963623image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:46.101806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:46.248075image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:46.488219image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:46.646443image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:46.801728image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:46.953485image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:47.090777image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:47.222073image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:47.346913image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:47.481122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:47.613735image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:47.743241image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:47.891192image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:48.024722image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:48.158479image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:48.291697image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:48.424059image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:48.567668image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:48.712785image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:48.863959image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:49.011935image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:49.152967image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:49.291945image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:49.437599image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:49.597094image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:49.769064image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:49.910482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:50.035946image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:50.173028image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:50.312287image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:50.448947image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:50.582624image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:50.834917image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:50.978318image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:51.110123image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:51.239135image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:51.376261image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:51.518301image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:51.665678image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:51.805150image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:51.945921image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:52.090070image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:52.226368image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:52.363515image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:52.501331image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:52.652031image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:52.791308image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:52.934848image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:53.060725image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:53.193849image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:53.321192image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:53.450633image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:53.589464image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:53.733654image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:53.888071image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:54.028723image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:54.166204image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:54.314513image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:54.457665image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:54.589655image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:54.722945image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:54.864322image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-10T10:40:54.998941image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-02-10T10:41:07.322703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-10T10:41:07.496548image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-10T10:41:07.689188image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-10T10:41:07.910931image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-10T10:41:08.141796image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-10T10:40:55.775693image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-10T10:40:56.883182image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-10T10:40:57.977902image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-10T10:40:58.335981image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Station NameMAC AddressOrg NameStart DateStart Time ZoneEnd DateEnd Time ZoneTotal Duration (hh:mm:ss)Charging Time (hh:mm:ss)Energy (kWh)GHG Savings (kg)Gasoline Savings (gallons)Port TypePort NumberPlug TypeAddress 1CityState/ProvincePostal CodeCountryLatitudeLongitudeCurrencyFeeEnded ByPlug In Event IdDriver Postal CodePark Duration (mins)Charge Duration (mins)IDPairlocation
0PALO ALTO CA / HAMILTON #1000D:6F00:015A:9D76City of Palo Alto2011-07-29 20:17:00PDT2011-07-29 23:20:00PDT1900-01-01 03:03:321900-01-01 01:54:036.2494572.6250.784Level 22J1772250 Hamilton AvePalo AltoCalifornia94301United States37.444572-122.160309USD0.0Plug Out at Vehicle395124.0184114.0037.444572x-122.160309
1PALO ALTO CA / HAMILTON #1000D:6F00:015A:9D76City of Palo Alto2011-07-30 00:00:00PDT2011-07-30 00:02:00PDT1900-01-01 00:02:061900-01-01 00:01:540.1065880.0450.013Level 22J1772250 Hamilton AvePalo AltoCalifornia94301United States37.444572-122.160309USD0.0Customer494301.022.0037.444572x-122.160309
2PALO ALTO CA / HAMILTON #1000D:6F00:015A:9D76City of Palo Alto2011-07-30 08:16:00PDT2011-07-30 12:34:00PDT1900-01-01 04:17:321900-01-01 04:17:2814.9517776.2801.876Level 22J1772250 Hamilton AvePalo AltoCalifornia94301United States37.444572-122.160309USD0.0Plug Out at Vehicle594301.0258257.0037.444572x-122.160309
3PALO ALTO CA / HAMILTON #1000D:6F00:015A:9D76City of Palo Alto2011-07-30 14:51:00PDT2011-07-30 16:55:00PDT1900-01-01 02:03:241900-01-01 02:02:587.1596433.0070.899Level 22J1772250 Hamilton AvePalo AltoCalifornia94301United States37.444572-122.160309USD0.0Customer694302.0123123.0037.444572x-122.160309
4PALO ALTO CA / HAMILTON #1000D:6F00:015A:9D76City of Palo Alto2011-07-30 18:51:00PDT2011-07-30 20:03:00PDT1900-01-01 01:11:241900-01-01 00:43:541.9577650.8220.246Level 22J1772250 Hamilton AvePalo AltoCalifornia94301United States37.444572-122.160309USD0.0Plug Out at Vehicle794043.07144.0037.444572x-122.160309
5PALO ALTO CA / HAMILTON #1000D:6F00:015A:9D76City of Palo Alto2011-07-31 04:33:00PDT2011-07-31 10:40:00PDT1900-01-01 06:06:191900-01-01 04:56:4717.1714637.2122.155Level 22J1772250 Hamilton AvePalo AltoCalifornia94301United States37.444572-122.160309USD0.0Customer994301.0366297.0037.444572x-122.160309
6PALO ALTO CA / HAMILTON #1000D:6F00:015A:9D76City of Palo Alto2011-07-31 12:25:00PDT2011-07-31 13:35:00PDT1900-01-01 01:09:541900-01-01 01:09:493.7991481.5960.477Level 22J1772250 Hamilton AvePalo AltoCalifornia94301United States37.444572-122.160309USD0.0Plug Out at Vehicle1094301.07070.0037.444572x-122.160309
7PALO ALTO CA / HAMILTON #1000D:6F00:015A:9D76City of Palo Alto2011-07-31 17:41:00PDT2011-07-31 22:31:00PDT1900-01-01 04:49:461900-01-01 04:41:1616.2385526.8202.038Level 22J1772250 Hamilton AvePalo AltoCalifornia94301United States37.444572-122.160309USD0.0Customer1194301.0290281.0037.444572x-122.160309
8PALO ALTO CA / HAMILTON #1000D:6F00:015A:9D76City of Palo Alto2011-08-01 19:11:00PDT2011-08-01 22:34:00PDT1900-01-01 03:23:341900-01-01 01:35:085.0412442.1170.633Level 22J1772250 Hamilton AvePalo AltoCalifornia94301United States37.444572-122.160309USD0.0Plug Out at Vehicle1394303.020495.0037.444572x-122.160309
9PALO ALTO CA / HAMILTON #1000D:6F00:015A:9D76City of Palo Alto2011-08-02 14:00:00PDT2011-08-02 15:13:00PDT1900-01-01 01:12:431900-01-01 01:12:334.1972171.7630.527Level 22J1772250 Hamilton AvePalo AltoCalifornia94301United States37.444572-122.160309USD0.0Plug Out at Vehicle1494403.07373.0037.444572x-122.160309

Last rows

Station NameMAC AddressOrg NameStart DateStart Time ZoneEnd DateEnd Time ZoneTotal Duration (hh:mm:ss)Charging Time (hh:mm:ss)Energy (kWh)GHG Savings (kg)Gasoline Savings (gallons)Port TypePort NumberPlug TypeAddress 1CityState/ProvincePostal CodeCountryLatitudeLongitudeCurrencyFeeEnded ByPlug In Event IdDriver Postal CodePark Duration (mins)Charge Duration (mins)IDPairlocation
142989PALO ALTO CA / HIGH #20024:B100:0002:5959City of Palo Alto2017-12-31 17:33:00PST2017-12-31 19:39:00PST1900-01-01 02:06:101900-01-01 01:47:169.3943.9461.179Level 22J1772528 High StPalo AltoCalifornia94301United States37.443344-122.163231USD2.16Customer220294404126107.04237.443344x-122.163231
142990PALO ALTO CA / CAMBRIDGE #10024:B100:0002:1329City of Palo Alto2017-12-31 17:34:00PST2017-12-31 19:17:00PST1900-01-01 01:42:071900-01-01 01:35:195.2492.2050.659Level 22J1772400 Cambridge AvePalo AltoCalifornia94306United States37.426155-122.146065USD1.21Plug Out at Vehicle61779430610295.03537.426155x-122.146065
142991PALO ALTO CA / WEBSTER #10024:B100:0002:4233City of Palo Alto2017-12-31 18:19:00PST2017-12-31 23:36:00PST1900-01-01 05:16:551900-01-01 02:35:567.9283.3300.995Level 22J1772520 Webster StPalo AltoCalifornia94301United States37.448284-122.158272USD5.88Plug Out at Vehicle109294020317156.04937.448284x-122.158272
142992PALO ALTO CA / CAMBRIDGE #10024:B100:0002:1329City of Palo Alto2017-12-31 18:22:00PST2017-12-31 22:18:00PST1900-01-01 03:55:331900-01-01 03:55:2013.4125.6331.683Level 21J1772400 Cambridge AvePalo AltoCalifornia94306United States37.426155-122.146065USD3.08CPS Server617894043236235.03537.426155x-122.146065
142993PALO ALTO CA / HAMILTON #20024:B100:0002:965DCity of Palo Alto2017-12-31 18:53:00PST2017-12-31 21:18:00PST1900-01-01 02:24:331900-01-01 02:15:066.4062.6900.804Level 22J1772250 Hamilton AvePalo AltoCalifornia94301United States37.444706-122.160324USD1.47Customer47594010145135.05237.444706x-122.160324
142994PALO ALTO CA / CAMBRIDGE #20024:B100:0002:13B7City of Palo Alto2017-12-31 19:58:00PST2017-12-31 20:19:00PST1900-01-01 00:20:511900-01-01 00:20:342.0630.8660.259Level 22J1772400 Cambridge AvePalo AltoCalifornia94306United States37.426182-122.146065USD0.47Plug Out at Vehicle1514779105946012121.03737.426182x-122.146065
142995PALO ALTO CA / BRYANT #50024:B100:0002:6D51City of Palo Alto2017-12-31 20:20:00PST2017-12-31 22:57:00PST1900-01-01 02:37:021900-01-01 02:36:5015.6816.5861.968Level 22J1772445 Bryant StPalo AltoCalifornia94301United States37.446690-122.162231USD3.61Plug Out at Vehicle90094555157157.04737.44669x-122.162231
142996PALO ALTO CA / HIGH #40024:B100:0002:1327City of Palo Alto2017-12-31 20:54:00PST2017-12-31 22:04:00PST1900-01-01 01:09:381900-01-01 01:09:213.8541.6190.484Level 21J1772528 High StPalo AltoCalifornia94301United States37.443138-122.163239USD0.89Plug Out at Vehicle1148943017069.04437.443138x-122.163239
142997PALO ALTO CA / WEBSTER #20024:B100:0002:050ECity of Palo Alto2017-12-31 21:00:00PST2017-12-31 22:56:00PST1900-01-01 01:55:171900-01-01 01:09:422.8541.1990.358Level 22J1772520 Webster StPalo AltoCalifornia94301United States37.448238-122.158203USD1.33Plug Out at Vehicle8649507011570.04837.448238x-122.158203
142998PALO ALTO CA / MPL #2000D:6F00:0154:EF9ACity of Palo Alto2017-12-31 23:35:00PST2018-01-01 07:18:00PST1900-01-01 07:42:261900-01-01 07:42:1347.39219.9055.948Level 21J17723700 Middlefield RdPalo AltoCalifornia94303United States37.422169-122.112480USD10.90Plug Out at Vehicle138494303462462.03037.422169x-122.11248

Duplicate rows

Most frequent

Station NameMAC AddressOrg NameStart DateStart Time ZoneEnd DateEnd Time ZoneTotal Duration (hh:mm:ss)Charging Time (hh:mm:ss)Energy (kWh)GHG Savings (kg)Gasoline Savings (gallons)Port TypePort NumberPlug TypeAddress 1CityState/ProvincePostal CodeCountryLatitudeLongitudeCurrencyFeeEnded ByPlug In Event IdPark Duration (mins)Charge Duration (mins)IDPairlocationcount
0PALO ALTO CA / BRYANT #2000D:6F00:009D:CFC3City of Palo Alto2017-03-10 14:07:00PST2017-03-10 15:53:00PST1900-01-01 01:46:011900-01-01 01:24:314.3850001.8420.550Level 22J1772Bryant StPalo AltoCalifornia94301United States37.446510-122.162140USD0.0Customer945310685.02037.44651x-122.162142
1PALO ALTO CA / HIGH #20024:B100:0002:5959City of Palo Alto2017-02-27 11:14:00PST2017-02-27 14:17:00PST1900-01-01 03:02:421900-01-01 02:33:458.5380003.5861.071Level 21J1772528 high stPalo AltoCalifornia94301United States37.443146-122.163017USD0.0Plug Out at Vehicle1488222853183154.04237.443146x-122.1630172
2PALO ALTO CA / MPL #6000D6F00015B7509City of Palo Alto2015-02-25 15:24:00PST2015-02-25 15:46:00PST1900-01-01 00:22:231900-01-01 00:22:101.1868640.4980.149Level 21J17723700 Middlefield RdPalo AltoCalifornia94303United States37.422138-122.113457USD0.0Plug Out at Vehicle4692222.0737.422138x-122.1134572